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\begin{abstract}

In this assignment we use Rapidminer 5.1 to develop models to select an
optimal subset of test examples to solicit from the 97NK mailing from the 
KDD Cup 1998 test data in order to maximize the donation profit. We develop 
two models for predicting two target variables: the probability of
donation and the amount of donation. We train these models on $95,412$ 
training examples using the Linear Regression operator and the Fast 
Large Margin operator with L2 Logistic kernel in Rapidminer. We then 
apply these models on the test data to obtain the probability of donation 
and the conditional estimated donation amount. We solicit only those examples
for which the estimated donation amount is greater than the cost of
solicitation. With the models we develop, we obtain a profit of \optprofit{} 
with \optsol{} solicitations.

\end{abstract}

